Data lab #8
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autokaggle/auto_ml.py
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| # TODO: Further clean the design of this file | ||
| class AutoKaggle(BaseEstimator): | ||
| pipeline = None |
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Move the class variables to instance variables.
autokaggle/auto_ml.py
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| p_hparams_base = None | ||
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| def __init__(self, config=None, **kwargs): | ||
| """ |
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Follow autokeras doc string style.
autokaggle/auto_ml.py
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| import hyperopt | ||
| from hyperopt import tpe, hp, fmin, Trials, STATUS_OK, STATUS_FAIL | ||
| from sklearn.model_selection import cross_val_score | ||
| from autokaggle.ensemblers import RankedEnsembler, StackingEnsembler |
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import modules instead of classes.
autokaggle/auto_ml.py
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| m_hparams_base = None | ||
| p_hparams_base = None | ||
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| def __init__(self, config=None, **kwargs): |
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Explicitly clarify all the arguments instead of using kwargs.
autokaggle/auto_ml.py
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| x: A numpy.ndarray instance containing the training data. | ||
| y: training label vector. | ||
| time_limit: remaining time budget. | ||
| data_info: meta-features of the dataset, which is an numpy.ndarray describing the |
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A list of strings. (specify the type)
autokaggle/auto_ml.py
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| self.pipeline.fit(x_train, y_train) | ||
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| def resample(self, x, y): | ||
| if self.config.balance_class_dist: |
| return x, y | ||
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| def subsample(self, x, y, sample_percent): | ||
| # TODO: Add way to balance the subsample |
autokaggle/auto_ml.py
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| return grid_train_x, grid_train_y | ||
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| def search(self, x, y, prep_space, model_space): | ||
| grid_train_x, grid_train_y = self.subsample(x, y, sample_percent=self.config.subsample_ratio) |
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set maximum line length to 85, and check with CI using flake 8.
autokaggle/auto_ml.py
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| np.random.shuffle(best_trials) | ||
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| if self.config.diverse_ensemble: | ||
| estimator_list = self.pick_diverse_estimators(best_trials, self.config.num_estimators_ensemble) |
autokaggle/auto_ml.py
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| return np.array(data_info) | ||
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| class AutoKaggleClassifier(AutoKaggle): |
autokaggle/auto_ml.py
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| return score_metric, skf | ||
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| class AutoKaggleRegressor(AutoKaggle): |
| self.ensembling_algo = hyperopt.rand.suggest if ensembling_algo == 'random' else hyperopt.tpe.suggest | ||
| self.num_p_hparams = num_p_hparams | ||
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| def update(self, options): |
autokaggle/config.py
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| setattr(self, k, v) | ||
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| knn_classifier_params = { |
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Use all capital letters for constants.
autokaggle/ensemblers.py
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| } | ||
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| class RankedEnsembler: |
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Extract a base class , function should raise not implemented error.
- Extend object class.
- Rename to RankEnsembleModel
- Doc strings.
| self.stacking_estimator = self.search(predictions, y_val) | ||
| self.stacking_estimator.fit(predictions, y_val) | ||
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| def search(self, x, y): |
autokaggle/preprocessor.py
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| LEVEL_HIGH = 32 | ||
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| class TabularPreprocessor(TransformerMixin): |
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